code: def normal_kl(mean1, logvar1, mean2, logvar2):
      """
      source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
      Compute the KL divergence between two gaussians.
      Shapes are automatically broadcasted, so batches can be compared to
      scalars, among other use cases.
      """
      tensor = None
      for obj in (mean1, logvar1, mean2, logvar2):
          if isinstance(obj, torch.Tensor):
              tensor = obj
              break
      assert (
          tensor is not None
      ), "at least one argument must be a Tensor"
  
      # Force variances to be Tensors. Broadcasting helps convert scalars to
      # Tensors, but it does not work for torch.exp().
      logvar1, logvar2 = [
          x
          if isinstance(x, torch.Tensor)
          else torch.tensor(x).to(tensor)
          for x in (logvar1, logvar2)
      ]
  
      return 0.5 * (
          -1.0
          + logvar2
          - logvar1
          + torch.exp(logvar1 - logvar2)
          + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
      )
  
file_path: NeMo/nemo/collections/multimodal/modules/stable_diffusion/distributions/distributions.py
imports: import numpy
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: normal_kl(mean1, logvar1, mean2, logvar2) and returns: unknown